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MIB Daily: Oil’s Surge to $86 and 5.16% Yields Hand the Fed Its Excuse — Energy Leads as Alphabet, Tesla Defend $489B in AI Debt

By RecessionALERT on July 22, 2026 in Reflections

Oil hit six-week highs — WTI +2.73%, Brent +3.32% — as an 11th night of US strikes on Iran, a Houthi Red Sea blockade threat, and a Caspian Pipeline attack compound at once, dragging gold to $4,140 while stocks slipped (S&P -0.14%, Nasdaq 100 -0.54%). Deutsche Bank and BofA now see 2-3 more Fed hikes after a weak 20-year auction pushed yields to 5.16%. Dell +9.32%, GE Vernova -8.69%, Palantir -6.10%. Alphabet and Tesla report after the bell.

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MIB: Iran Holds Hormuz, Section 301 Targets 16 Nations, Private Credit Cracks & FOMC March 18 in Crosshairs

By RecessionALERT on March 12, 2026 in Reflections

Iran’s new supreme leader vows Hormuz stays shut; oil briefly tops $100/bbl as IEA’s 400M barrel release fails. S&P 500 -1.52% to YTD low; VIX spikes to 27.29. Trump launches Section 301 probes into 16 nations as Canada’s 25% steel tariffs lock in. Private credit cracks: Morgan Stanley gates $8B fund, Deutsche Bank flags $30B exposure. Dollar General -7.8% despite earnings beat. All eyes on FOMC March 17-18 — dot plot in spotlight.

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IMPORTANT DISCLAIMERS: Although many of our models rely heavily on back-testing, optimisation and probability methods, please note that past performance is NO GUARANTEE for future returns. No system devised by man can predict the future, let alone the future of the markets and economies. What we have are sets of mathematical models that use historical data and varying theories to pinpoint places in time when the STATISTICAL LIKELYHOOD of calling the start and end to recessions is the greatest. In the midst of all the best mathematical models, you can have geo-political events, wars, terrorist attacks, natural disasters and even nuclear accidents that tip everything upside down. This is always a risk factor you need to factor in on any recession call no matter how confident you are in a signal. The SuperIndexes are created through mathematical and statistical optimisation techniques that best fit the historical NBER data. There is no guarantee that out-of-sample performance will match that of prior in-sample performance.